
TL;DR
This paper empirically analyzes how scientific adoption of large language models (LLMs) evolves over time, revealing a pattern of rapid rise and decline that is increasingly compressed with each new release.
Contribution
It provides the first large-scale empirical account of LLM adoption trajectories, highlighting the rapid and compressed lifecycle patterns in scientific usage.
Findings
Adoption follows an inverted-U pattern called the 'scientific adoption curve'
Each new model release shortens the time to peak adoption by 27%
Release timing predicts lifecycle dynamics more than model architecture or size
Abstract
Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released. We provide the first large-scale empirical account of how scientists adopt and abandon language models over time. We track 62 LLMs across over 108k citing papers (2018-2025), each with at least three years of post-release data, and classify every citation as active adoption or background reference to construct per-model adoption trajectories that raw citation counts cannot resolve. We find three regularities. First, scientific adoption follows an inverted-U trajectory: usage rises after release, peaks, and declines as newer models appear, a pattern we term the \textit{scientific adoption curve}. Second, this curve is compressing: each additional release year is associated with a 27\% reduction in time-to-peak adoption (), robust to…
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